| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - tactile |
| --- |
| |
| # 📦 FreeTacman |
| ## Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation [ICRA 2026] |
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| ## 🎯 Overview |
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| This dataset supports the paper **[FreeTacman: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation](http://arxiv.org/abs/2506.01941)**. |
| It contains a large-scale, high-precision visuo-tactile manipulation dataset with over 3000k visuo-tactile image pairs, more than 10k trajectories across 50 tasks. |
| We provide 🤗 Script ([Hugging Face](https://huggingface.co/datasets/OpenDriveLab/FreeTacMan)) and 👾 Script ([ModelScope](https://www.modelscope.cn/datasets/OpenDriveLab/FreeTacMan)) (users in China) for downloading the dataset. |
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| Please refer to our 🚀 [Website](http://opendrivelab.com/freetacman) | 📄 [Paper](http://arxiv.org/abs/2506.01941) | 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan) | 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) | 📺 [Video](https://opendrivelab.github.io/FreeTacMan/landing/FreeTacMan_demo_video.mp4) | 🌐 [X](https://x.com/OpenDriveLab/status/1930234855729836112) for more details. |
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| ## 🔬 Potential Applications |
| The FreeTacman dataset enables diverse research directions in visuo-tactile learning and manipulation: |
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| - **System Reproduction**: For researchers interested in hardware implementation, you can reproduce FreeTacMan from scratch using our 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) and 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan). |
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| - **Multimodal Imitation Learning**: Transfer to other LED-based tactile sensors (such as GelSight) for developing robust multimodal imitation learning frameworks. |
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| - **Tactile-aware Grasping**: Utilize the dataset for pre-training tactile representation models and developing tactile-aware reasoning systems. |
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| - **Simulation-to-Real Transfer**: Leverage the dynamic tactile interaction sequences to enhance tactile simulation fidelity, significantly reducing the sim2real gap. |
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| ## 📂 Dataset Structure |
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| The dataset is organized into 50 task categories, each containing: |
| - **Video files**: Synchronized video recordings from the wrist-mounted and visuo-tactile cameras for each demonstration |
| - **Trajectory files**: Detailed tracking data for tool center point pose and gripper distance |
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| ## 🧾 Data Format |
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| ### Video Files |
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| - **Format**: MP4 |
| - **Views**: Wrist-mounted camera and visuo-tactile camera perspectives per demonstration |
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| ### Trajectory Files |
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| Each trajectory file contains the following data columns: |
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| #### Timestamp |
| - `timestamp` - Unix Timestamp |
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| #### Tool Center Point (TCP) Data |
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| - `TCP_pos_x`, `TCP_pos_y`, `TCP_pos_z` - TCP position |
| - `TCP_euler_x`, `TCP_euler_y`, `TCP_euler_z` - TCP orientation (euler angles) |
| - `quat_w`, `quat_x`, `quat_y`, `quat_z` - TCP orientation (quaternion representation) |
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| #### Gripper Data |
| - `gripper_distance` - Gripper opening distance |
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| ## 📝 Citation |
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| If you use this dataset in your research, please cite: |
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| ```bibtex |
| @article{wu2025freetacman, |
| title={FreeTacMan: Robot-free visuo-tactile data collection system for contact-rich manipulation}, |
| author={Wu, Longyan and Yu, Checheng and Ren, Jieji and Chen, Li and Jiang, Yufei and Huang, Ran and Gu, Guoying and Li, Hongyang}, |
| journal={IEEE International Conference on Robotics and Automation}, |
| year={2026} |
| } |
| ``` |
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| ## 💼 License |
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| This dataset is released under the MIT License. See LICENSE file for details. |
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| ## 📧 Contact |
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| For questions or issues regarding the dataset, please contact: Longyan Wu (im.longyanwu@gmail.com). |
